Microtargeting for conservation

Alexander L. Metcalf, Conor N. Phelan, Cassandra Pallai, Michael Norton, Ben Yuhas, James C. Finley, Allyson Muth

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Widespread human action and behavior change is needed to achieve many conservation goals. Doing so at the requisite scale and pace will require the efficient delivery of outreach campaigns. Conservation gains will be greatest when efforts are directed toward places of high conservation value (or need) and tailored to critical actors. Recent strategic conservation planning has relied primarily on spatial assessments of biophysical attributes, largely ignoring the human dimensions. Elsewhere, marketers, political campaigns, and others use microtargeting—predictive analytics of big data—to identify people most likely to respond positively to particular messages or interventions. Conservationists have not yet widely capitalized on these techniques. To investigate the effectiveness of microtargeting to improve conservation, we developed a propensity model to predict restoration behavior among 203,645 private landowners in a 5,200,000 ha study area in the Chesapeake Bay Watershed (U.S.A.). To isolate the additional value microtargeting may offer beyond geospatial prioritization, we analyzed a new high-resolution land-cover data set and cadastral data to identify private owners of riparian areas needing restoration. Subsequently, we developed and evaluated a restoration propensity model based on a database of landowners who had conducted restoration in the past and those who had not (n = 4978). Model validation in a parallel database (n = 4989) showed owners with the highest scorers for propensity to conduct restoration (i.e., top decile) were over twice as likely as average landowners to have conducted restoration (135%). These results demonstrate that microtargeting techniques can dramatically increase the efficiency and efficacy of conservation programs, above and beyond the advances offered by biophysical prioritizations alone, as well as facilitate more robust research of many social–ecological systems.

Original languageEnglish (US)
Pages (from-to)1141-1150
Number of pages10
JournalConservation Biology
Volume33
Issue number5
DOIs
StatePublished - Oct 1 2019

Fingerprint

landowners
prioritization
landowner
outreach
conservation programs
Chesapeake Bay
riparian areas
model validation
behavior change
land cover
planning
conservation planning
methodology
restoration
watershed

All Science Journal Classification (ASJC) codes

  • Ecology, Evolution, Behavior and Systematics
  • Ecology
  • Nature and Landscape Conservation

Cite this

Metcalf, A. L., Phelan, C. N., Pallai, C., Norton, M., Yuhas, B., Finley, J. C., & Muth, A. (2019). Microtargeting for conservation. Conservation Biology, 33(5), 1141-1150. https://doi.org/10.1111/cobi.13315
Metcalf, Alexander L. ; Phelan, Conor N. ; Pallai, Cassandra ; Norton, Michael ; Yuhas, Ben ; Finley, James C. ; Muth, Allyson. / Microtargeting for conservation. In: Conservation Biology. 2019 ; Vol. 33, No. 5. pp. 1141-1150.
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Metcalf, AL, Phelan, CN, Pallai, C, Norton, M, Yuhas, B, Finley, JC & Muth, A 2019, 'Microtargeting for conservation', Conservation Biology, vol. 33, no. 5, pp. 1141-1150. https://doi.org/10.1111/cobi.13315

Microtargeting for conservation. / Metcalf, Alexander L.; Phelan, Conor N.; Pallai, Cassandra; Norton, Michael; Yuhas, Ben; Finley, James C.; Muth, Allyson.

In: Conservation Biology, Vol. 33, No. 5, 01.10.2019, p. 1141-1150.

Research output: Contribution to journalArticle

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Metcalf AL, Phelan CN, Pallai C, Norton M, Yuhas B, Finley JC et al. Microtargeting for conservation. Conservation Biology. 2019 Oct 1;33(5):1141-1150. https://doi.org/10.1111/cobi.13315